Towards universal health coverage: What are the system requirements for effective large-scale community health worker programmes?

Against the background of efforts to strengthen health systems for universal health coverage and health equity, many African countries have been relying on lay members of the community, often referred to as community health workers (CHWs), to deliver primary healthcare services. Growing demand and great variability in definitions, roles, governance and funding of CHWs have prompted the need to revisit CHW programmes and provide guidance on the implementation of successful programmes at scale. Drawing on the synthesised evidence from two extensive literature reviews, this article determines foundational elements of functioning CHW programmes, focusing in particular on the systems requirements of large-scale programmes. It makes recommendations for the effective development of large-scale CHW programmes

“Top-down bottom-up” estimation of per capita cost of new-born care interventions in four regions of Ghana:beyond implementation to scalability and sustainability

Background: Limited financial, human and material health resources coupled with increasing demand for new-born care services require efficiency in health systems to maximize the available sources for improved health outcomes. Making Every Baby Count Initiative (MEBCI) implemented by local and international partners in 2013 in Ghana aimed at attaining neonatal mortality of 21 per 1000 livebirths by 2018 in four administrative regions in Ghana. MEBCI interventions benefited 4027 health providers, out of which 3453 (86%) were clinical healthcare staff.Objective: Determine the per capita cost of the MEBCI interventions towards enhancing new-born care best practices through capacity trainings for frontline clinical and non-clinical staff.Methods: Parameters for determining per capita cost of the new-born care interventions were estimated using expenditure on trainings, supervisions, monitoring and evaluation, advocacy, administrative/services and medical logistics. Data collection started in October 2017 and ended in September 2018. Data sources for the per capita cost estimations were invoices, expense reports and ledger books at the national, regional and district levels of the health system.Results: Total of 4027 healthcare providers benefited from the MEBCI training activities comprising of 3453 clinical staff and 574 non-clinical personnel. Cumulative cost of implementing the MEBCI interventions did not necessarily match the cost per capita in staff capacity building; average cost per capita for all staff (clinical and non-clinical staff) was approximately US$982 compared to a per capita cost of US$ 799 for training only core clinical staff. Average cost per capita for all regions was approximately US$965 for all staff compared to US$ 777 per capita cost for only clinical staff. Per capita cost of training was relatively lower in regions with more staff than regions with lower numbers, perhaps due to economies of scale.Conclusion: The MEBCI intervention had a wide coverage in terms of training for frontline healthcare providers albeit the associated cost may be potentially unsustainable for Ghana’s health system. Emerging digital training platforms could be leveraged to reduce per capita cost of training. Large-scale on-site batch-training approach could also be replaced with facility-based workshops using training of trainers (TOTs) approach to promote efficiency

“Top-down bottom-up” estimation of per capita cost of new-born care interventions in four regions of Ghana:beyond implementation to scalability and sustainability

Background: Limited financial, human and material health resources coupled with increasing demand for new-born care services require efficiency in health systems to maximize the available sources for improved health outcomes. Making Every Baby Count Initiative (MEBCI) implemented by local and international partners in 2013 in Ghana aimed at attaining neonatal mortality of 21 per 1000 livebirths by 2018 in four administrative regions in Ghana. MEBCI interventions benefited 4027 health providers, out of which 3453 (86%) were clinical healthcare staff.Objective: Determine the per capita cost of the MEBCI interventions towards enhancing new-born care best practices through capacity trainings for frontline clinical and non-clinical staff.Methods: Parameters for determining per capita cost of the new-born care interventions were estimated using expenditure on trainings, supervisions, monitoring and evaluation, advocacy, administrative/services and medical logistics. Data collection started in October 2017 and ended in September 2018. Data sources for the per capita cost estimations were invoices, expense reports and ledger books at the national, regional and district levels of the health system.Results: Total of 4027 healthcare providers benefited from the MEBCI training activities comprising of 3453 clinical staff and 574 non-clinical personnel. Cumulative cost of implementing the MEBCI interventions did not necessarily match the cost per capita in staff capacity building; average cost per capita for all staff (clinical and non-clinical staff) was approximately US$982 compared to a per capita cost of US$ 799 for training only core clinical staff. Average cost per capita for all regions was approximately US$965 for all staff compared to US$ 777 per capita cost for only clinical staff. Per capita cost of training was relatively lower in regions with more staff than regions with lower numbers, perhaps due to economies of scale.Conclusion: The MEBCI intervention had a wide coverage in terms of training for frontline healthcare providers albeit the associated cost may be potentially unsustainable for Ghana’s health system. Emerging digital training platforms could be leveraged to reduce per capita cost of training. Large-scale on-site batch-training approach could also be replaced with facility-based workshops using training of trainers (TOTs) approach to promote efficiency

Absence of an association between Plasmodium falciparum infection and post-ivermectin Loa-related non-neurologic serious adverse events.

Although ivermectin treatment can induce serious adverse events (SAEs) in individuals harboring high Loa loa microfilaremia (mf), not all patients with high mf levels develop such reactions, suggesting that cofactors may be involved. A study was conducted in Cameroon to investigate the possible role of Plasmodium coinfection at the time of ivermectin treatment in the development of SAEs. Before their first ivermectin treatment, thick smears were obtained from 4,175 individuals to determine the burden of Plasmodium sp., L. loa, and Mansonella perstans. After treatment, 18 (4.3 per 1,000) patients developed a non-neurologic SAE. Logistic regression analysis, adjusting for age, sex, P. falciparum infection, and M. perstans infection intensities, confirmed that L. loa mf was the main risk factor for SAEs. We found no evidence that coinfection with P. falciparum at the time of ivermectin treatment was associated with the occurrence of Loa-related SAEs in this population

Expectations of healthcare quality: A cross-sectional study of internet users in 12 low- and middle-income countries.

BackgroundHigh satisfaction with healthcare is common in low- and middle-income countries (LMICs), despite widespread quality deficits. This may be due to low expectations because people lack knowledge about what constitutes good quality or are resigned about the quality of available services.Methods and findingsWe fielded an internet survey in Argentina, China, Ghana, India, Indonesia, Kenya, Lebanon, Mexico, Morocco, Nigeria, Senegal, and South Africa in 2017 (N = 17,996). It included vignettes describing poor-quality services-inadequate technical or interpersonal care-for 2 conditions. After applying population weights, most of our respondents lived in urban areas (59%), had finished primary school (55%), and were under the age of 50 (75%). Just over half were men (51%), and the vast majority reported that they were in good health (73%). Over half (53%) of our study population rated the quality of vignettes describing poor-quality services as good or better. We used multilevel logistic regression and found that good ratings were associated with less education (no formal schooling versus university education; adjusted odds ratio [AOR] 2.22, 95% CI 1.90-2.59, P ConclusionsMajorities of the internet-using public in 12 LMICs have low expectations of healthcare quality as evidenced by high ratings given to poor-quality care. Low expectations of health services likely dampen demand for quality, reduce pressure on systems to deliver quality care, and inflate satisfaction ratings. Policies and interventions to raise people's expectations of the quality of healthcare they receive should be considered in health system quality reforms